Alumna: Nicole Muñoz
Profesor: Víctor Macías
Ayudante: Gabriel Cabrera
library(quantmod)
library(dplyr)
library(plotly)
library(gridExtra)
library(tidyverse)
library(tidyquant)
library(forcats)
library(devtools)acciones <- c("AAPL","MSFT")
data <- tq_get(acciones,
get = "stock.prices",
from = "2000-01-01",
to = "2018-08-30",
periodicity = "monthly")
data <- data.frame(data$symbol, data$date, data$close)
data <- data%>%rename(accion=data.symbol,
fecha=data.date,
precio=data.close)
finance = function(x,return=c('yes','no'),plot=c('type 1','type 2'),normal=c('yes','no')){
#retornos log
if (return=='yes'){
retornoslog <- x %>%
group_by(accion) %>%
tq_transmute(select = precio,
mutate_fun = periodReturn,
period = "monthly",
type = "log",
col_rename = "retornos")
dataA <- subset(retornoslog, accion=="AAPL")
dataM <- subset(retornoslog, accion=="MSFT")
dataA <- dataA %>% mutate(retcumA = cumsum(retornos))
dataM <- dataM %>% mutate(retcumM = cumsum(retornos))
#grafico 11:retornos log
ifelse(plot=='type 1',
g11 <- plot_ly(dataA, x = ~fecha) %>%
add_lines(y = ~dataA$retornos, name = "APPLE", line = list(color = 'rgb(19,135,171)', width = 2)) %>%
add_lines(y = ~dataM$retornos, name = "MICROSOFT", line = list(color = 'rgb(153,171,254)', width = 2)) %>%
layout(
title = "RETORNOS (logarítmicos) APPLE & MICROSOFT",
xaxis = list(
rangeselector = list(
buttons = list(
list(
count = 3,
label = "3 mo",
step = "month",
stepmode = "backward"),
list(
count = 6,
label = "6 mo",
step = "month",
stepmode = "backward"),
list(
count = 1,
label = "1 yr",
step = "year",
stepmode = "backward"),
list(
count = 1,
label = "YTD",
step = "year",
stepmode = "todate"),
list(step = "all"))),
rangeslider = list(type = "Fecha")),
yaxis = list(title = "Retornos")),
ifelse(
plot=='type 2',
g12 <- plot_ly(dataA, x = ~fecha) %>%
add_lines(y = ~dataA$retcumA, name = "APPLE", line = list(color = 'rgb(19,135,171)', width = 2)) %>%
add_lines(y = ~dataM$retcumM, name = "MICROSOFT", line = list(color = 'rgb(153,171,254)', width = 2)) %>%
layout(
title = "RETORNOS ACUMULADOS (logarítmicos) APPLE & MICROSOFT",
xaxis = list(
rangeselector = list(
buttons = list(
list(
count = 3,
label = "3 mo",
step = "month",
stepmode = "backward"),
list(
count = 6,
label = "6 mo",
step = "month",
stepmode = "backward"),
list(
count = 1,
label = "1 yr",
step = "year",
stepmode = "backward"),
list(
count = 1,
label = "YTD",
step = "year",
stepmode = "todate"),
list(step = "all"))),
rangeslider = list(type = "Fecha")),
yaxis = list(title = "Retornos"))
)#ifelse dentro de los graficos
)#fin ifelse graficos
jlog = by(retornoslog,retornoslog$accion,
function(x){
n=length(x$retornos)
mean = sum(x$retornos)/n
skewness = ((sum(x$retornos-mean)^3)/n)/((sum(x$retornos-mean)^2)/n)^(3/2)
kurtosis = ((sum(x$retornos-mean)^4)/n)/((sum(x$retornos-mean)^2)/n)^2
JB = n*(((skewness^2)/6)+(((kurtosis-3)^2)/24))
j = paste('p-value =',1 - pchisq(JB,df = 2),ifelse(1 - pchisq(JB,df = 2)<0.05,
', se rechaza la hipotesis nula de normalidad para los retornos de Apple',
', no se rechaza la hipotesis nula de normalidad para los retornos de Microsoft'))})
}#fin primer if si return=yes
#retornos aritmeticos
else if (return=='no'){
retornosimple <- x %>%
group_by(accion) %>%
tq_transmute(select = precio,
mutate_fun = periodReturn,
period = "monthly",
type = "arithmetic",
col_rename = "retornos")
dataA1 <- subset(retornosimple, accion=="AAPL")
dataM1 <- subset(retornosimple, accion=="MSFT")
dataA1 <- dataA1 %>% mutate(retcumA = cumsum(retornos))
dataM1 <- dataM1 %>% mutate(retcumM = cumsum(retornos))
ifelse(plot=='type 1',
g21 <- plot_ly(dataA1, x = ~fecha) %>%
add_lines(y = ~dataA1$retornos, name = "APPLE", line = list(color = 'rgb(19,135,171)', width = 2)) %>%
add_lines(y = ~dataM1$retornos, name = "MICROSOFT", line = list(color = 'rgb(153,171,254)', width = 2)) %>%
layout(
title = "RETORNOS (aritméticos) APPLE & MICROSOFT",
xaxis = list(
rangeselector = list(
buttons = list(
list(
count = 3,
label = "3 mo",
step = "month",
stepmode = "backward"),
list(
count = 6,
label = "6 mo",
step = "month",
stepmode = "backward"),
list(
count = 1,
label = "1 yr",
step = "year",
stepmode = "backward"),
list(
count = 1,
label = "YTD",
step = "year",
stepmode = "todate"),
list(step = "all"))),
rangeslider = list(type = "Fecha")),
yaxis = list(title = "Retornos")),
ifelse(plot=='type 2',
g22 <- plot_ly(dataA1, x = ~fecha) %>%
add_lines(y = ~dataA1$retcumA, name = "APPLE", line = list(color = 'rgb(19,135,171)', width = 2)) %>%
add_lines(y = ~dataM1$retcumM, name = "MICROSOFT", line = list(color = 'rgb(153,171,254)', width = 2)) %>%
layout(
title = "RETORNOS ACUMULADOS (aritméticos) APPLE & MICROSOFT",
xaxis = list(
rangeselector = list(
buttons = list(
list(
count = 3,
label = "3 mo",
step = "month",
stepmode = "backward"),
list(
count = 6,
label = "6 mo",
step = "month",
stepmode = "backward"),
list(
count = 1,
label = "1 yr",
step = "year",
stepmode = "backward"),
list(
count = 1,
label = "YTD",
step = "year",
stepmode = "todate"),
list(step = "all"))),
rangeslider = list(type = "Fecha")),
yaxis = list(title = "Retornos"))
)#fin ifelse grafico tipo 2
) #fin primer ifelse grafico tipo 1
j_arit= by(retornosimple,retornosimple$accion,
function(x){
n=length(x$retornos)
mean = sum(x$retornos)/n
skewness = ((sum(x$retornos-mean)^3)/n)/((sum(x$retornos-mean)^2)/n)^(3/2)
kurtosis = ((sum(x$retornos-mean)^4)/n)/((sum(x$retornos-mean)^2)/n)^2
JB = n*(((skewness^2)/6)+(((kurtosis-3)^2)/24))
j = paste('p-value =',1 - pchisq(JB,df = 2),
ifelse(1 - pchisq(JB,df = 2)<0.05,
', se rechaza la hipotesis nula de normalidad para los retornos de Apple',
', no se rechaza la hipotesis nula de normalidad para los retornos de Microsoft'))})
}#fin else if retornos=no
#debemos general el resultado normal==no y dado que en ambos casos se realiza el test, programamos la salida:
no="Se realizó test de Normalidad, ocultando su resultado"
#debemos generar todas las posibles salidas
ifelse(return=="yes"& plot=='type 1' & normal=="yes",return(list(g11,jlog)),
ifelse(return=='yes' & plot=='type 2' & normal=="yes",return(list(g12,jlog)),
ifelse(return=='yes' & plot=='type 1' & normal=="no",return(list(g11,no)),
ifelse(return=='yes' & plot=='type 2' & normal=="no",return(list(g12,no)),
ifelse(return=='no' & plot=='type 1' & normal=="yes",return(list(g21,j_arit)),
ifelse(return=='no' & plot=='type 2' & normal=="yes",return(list(g22,j_arit)),
ifelse(return=='no' & plot=='type 1' & normal=="no",return(list(g21,no)),
ifelse(return=='no' & plot=='type 2' & normal=="no",return(list(g22,no))))))))
))
}#fin funcionfinance(data,"yes","type 1","yes")[[1]]
[[2]]
retornoslog$accion: AAPL
[1] "p-value = 0 , se rechaza la hipotesis nula de normalidad para los retornos de Apple"
---------------------------------------------------------
retornoslog$accion: MSFT
[1] "p-value = 0 , se rechaza la hipotesis nula de normalidad para los retornos de Apple"finance(data,"no","type 1","yes")[[1]]
[[2]]
retornosimple$accion: AAPL
[1] "p-value = 0 , se rechaza la hipotesis nula de normalidad para los retornos de Apple"
---------------------------------------------------------
retornosimple$accion: MSFT
[1] "p-value = 0 , se rechaza la hipotesis nula de normalidad para los retornos de Apple"finance(data, "yes", "type 2", "yes")[[1]]
[[2]]
retornoslog$accion: AAPL
[1] "p-value = 0 , se rechaza la hipotesis nula de normalidad para los retornos de Apple"
---------------------------------------------------------
retornoslog$accion: MSFT
[1] "p-value = 0 , se rechaza la hipotesis nula de normalidad para los retornos de Apple"finance(data, "no", "type 2", "yes")[[1]]
[[2]]
retornosimple$accion: AAPL
[1] "p-value = 0 , se rechaza la hipotesis nula de normalidad para los retornos de Apple"
---------------------------------------------------------
retornosimple$accion: MSFT
[1] "p-value = 0 , se rechaza la hipotesis nula de normalidad para los retornos de Apple"